Specifications Compared
| Spec | RTX-4070 | V100 |
|---|---|---|
| TDP | 200W | 300W |
| VRAM | 12 GB | 16-32 GB |
| CUDA Cores | 5,888 | 5,120 |
| Memory Type | GDDR6X | HBM2 |
| Architecture | Ada Lovelace | Volta |
| Form Factors | PCIe | SXM2, PCIe |
| Interconnect | NVLink, PCIe 3.0 | |
| Tensor Cores | 184 | 640 |
| FP16 Performance | 29.1 TFLOPS | 125 TFLOPS |
| FP32 Performance | 29.1 TFLOPS | 15.7 TFLOPS |
| INT8 Performance | 466 TOPS | |
| Memory Bandwidth | 504 GB/s | 900 GB/s |
Performance Analysis
FP16 performance heavily favors the V100 at 125 TFLOPS over the RTX 4070 SUPER's 35.5 TFLOPS, enabling faster mixed-precision training and inference on large models where tensor core utilization dominates. FP32 reverses this: RTX 4070 SUPER achieves 35.5 TFLOPS against V100's 15.7 TFLOPS, suiting single-precision workloads like traditional simulations or certain inference pipelines. Memory configurations dictate real-world scalability: V100's 32 GB HBM2 and 900 GB/s bandwidth support larger batch sizes in memory-bound tasks, such as training LLMs with sequences exceeding what 12 GB GDDR6X at 504 GB/s permits on RTX 4070 SUPER. Power efficiency tilts toward RTX 4070 SUPER's 220 W TDP, allowing more units per rack versus V100's 300 W, reducing operational costs in dense deployments. Interconnects matter too: V100's NVLink excels in multi-GPU scaling, absent on PCIe-only RTX 4070 SUPER.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
RTX 4070 SUPER
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA GeForce RTX 4070 Ti 12GB VRAM | 12GB | 6 vCPU 30GB RAM | 🌍global | $0.50/GPU/hr |
Tesla V100 32GB
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 TensorDock | NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 0 vCPU 0GB RAM | Texas | $0.19/GPU/hr | Available | ||
TensorDock | NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 0 vCPU 0GB RAM | New York City | $0.19/GPU/hr | Available | ||
TensorDock | NVIDIA Tesla V100 32GB 32GB VRAM | 32GB | 0 vCPU 0GB RAM | Texas | $0.29/GPU/hr | Available | ||
TensorDock | NVIDIA Tesla V100 32GB 32GB VRAM | 32GB | 0 vCPU 0GB RAM | New York City | $0.29/GPU/hr | Available | ||
 Lambda Labs | 8×NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 88 vCPU 448GB RAM 6041GB Storage | Texas | $0.79/GPU/hr $6.32/hr total (8×) | Available |
When to Choose the RTX 4070 SUPER
Select the RTX 4070 SUPER for balanced workloads demanding strong FP32 performance at 35.5 TFLOPS, such as graphics rendering, gaming servers, or inference on models fitting within 12 GB VRAM. Its 220 W TDP and Ada Lovelace features enable efficient local or edge deployments where power and modernity matter. PCIe form factor simplifies consumer and small-scale cloud integration without specialized infrastructure.
When to Choose the Tesla V100 32GB
The V100 32GB excels in high-memory scenarios leveraging 32 GB HBM2 and 900 GB/s bandwidth for large-batch training or simulations. Its 125 TFLOPS FP16 suits legacy AI frameworks optimized for Volta tensor cores. NVLink and cloud availability from $0.29/hr make it ideal for multi-GPU HPC clusters.
Use Cases
V100's 125 TFLOPS FP16 and 32 GB HBM2 with 900 GB/s bandwidth handle large models and batches better than RTX 4070 SUPER's 12 GB and 35.5 TFLOPS.
RTX 4070 SUPER's 35.5 TFLOPS FP32 and modern Ada architecture optimize batched inference within 12 GB VRAM limits more efficiently than V100's 15.7 TFLOPS FP32.
RTX 4070 SUPER suits smaller datasets with 35.5 TFLOPS balance and 220 W TDP; V100 fits larger ones via 32 GB VRAM and 125 TFLOPS FP16.
RTX 4070 SUPER's Ada Lovelace optimizations and 504 GB/s bandwidth accelerate image generation efficiently at 35.5 TFLOPS FP16/FP32.
V100's 900 GB/s bandwidth and NVLink support large-scale simulations requiring 32 GB VRAM over RTX 4070 SUPER's constraints.
Frequently Asked Questions
Which GPU has more VRAM: RTX 4070 SUPER or V100 32GB?▾
The V100 32GB provides 32 GB HBM2, exceeding the RTX 4070 SUPER's 12 GB GDDR6X. This enables V100 to manage larger models. Bandwidth also favors V100 at 900 GB/s versus 504 GB/s.
Is RTX 4070 SUPER faster in FP32 than V100?▾
RTX 4070 SUPER delivers 35.5 TFLOPS FP32, surpassing V100's 15.7 TFLOPS. This benefits FP32-heavy tasks like some inference. FP16 reverses: V100 at 125 TFLOPS beats 35.5 TFLOPS.
What is the power consumption of RTX 4070 SUPER vs V100?▾
RTX 4070 SUPER has a 220 W TDP, lower than V100's 300 W. Lower power aids efficiency in multi-GPU setups. This impacts cloud costs indirectly.
Does V100 have better cloud pricing than RTX 4070 SUPER?▾
V100 32GB offers start at $0.29/hr, averaging $1.01/hr across 44 providers. RTX 4070 SUPER currently has no live cloud offers listed. Availability drives V100's edge.
RTX 4070 SUPER vs V100 for AI training?▾
V100's 125 TFLOPS FP16 and 32 GB VRAM suit large-scale training better. RTX 4070 SUPER excels in efficient, smaller-scale runs with 35.5 TFLOPS balance. Choose based on model size.
What architectures do these GPUs use?▾
RTX 4070 SUPER uses Ada Lovelace from 2023; V100 uses Volta from 2017. Ada offers modern features like improved RT cores. Volta emphasizes datacenter tensor performance.
Which is cheaper to rent, the RTX 4070 or the V100?▾
Cloud rental prices for both the RTX 4070 and V100 vary by provider, configuration, and availability. This page shows live pricing from 25+ providers updated every 60 seconds. Scroll to the Live Cloud Pricing section to compare current rates.
How much VRAM does the RTX 4070 have compared to the V100?▾
The RTX 4070 has 12 GB of GDDR6X memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find RTX 4070 and V100 GPUs available to rent right now?▾
Yes. This page shows real-time availability across 25+ cloud GPU providers. The Live Cloud Pricing section displays only in-stock offers with current pricing.
What is the main difference between the RTX 4070 and the V100?▾
The RTX 4070 uses the Ada Lovelace architecture (2023) while the V100 uses Volta (2017). The V100 delivers 4.3x the FP16 throughput and 1.8x the memory bandwidth of the RTX 4070.